Using HunyuanDiT IP-Adapter

Instructions

The dependencies and installation are basically the same as the base model, and we use the module weights for training. Download the model using the following commands:

cd HunyuanDiT
# Use the huggingface-cli tool to download the model.
# We recommend using module weights as the base model for IP-Adapter inference, as our provided pretrained weights are trained on them.
huggingface-cli download Tencent-Hunyuan/IP-Adapter/ipa.pt --local-dir ./ckpts/t2i/model
huggingface-cli download Tencent-Hunyuan/IP-Adapter/clip_img_encoder.pt --local-dir ./ckpts/t2i/model/clip_img_encoder

# Quick start
python3 sample_ipadapter.py --infer-mode fa --ref-image-path ipadapter/input/tiger.png --i-scale 1.0 --prompt 一只老虎在海洋中游泳，背景是海洋。构图方式是居中构图，呈现了动漫风格和文化，营造了平静的氛围。 --infer-steps 100 --is-ipa True --load-key module

Examples of ref input and IP-Adapter results are as follows:

Training

We provide base model weights for IP-Adapter training, you can use module weights for IP-Adapter training.

Here is an example, we load the module weights into the main model and conduct IP-Adapter training.

If apply multiple resolution training, you need to add the --multireso and --reso-step 64 parameter.

task_flag="IP_Adapter" # the task flag is used to identify folders. # checkpoint root for resume
index_file=path/to/your/index_file
results_dir=./log_EXP # save root for results
batch_size=1 # training batch size
image_size=1024 # training image resolution
grad_accu_steps=1 # gradient accumulation
warmup_num_steps=0 # warm-up steps
lr=0.0001 # learning rate
ckpt_every=10 # create a ckpt every a few steps.
ckpt_latest_every=10000 # create a ckpt named `latest.pt` every a few steps.
ckpt_every_n_epoch=2 # create a ckpt every a few epochs.
epochs=8 # total training epochs

PYTHONPATH=. \
sh $(dirname "$0")/run_g_ipadapter.sh \
 --task-flag ${task_flag} \
 --noise-schedule scaled_linear --beta-start 0.00085 --beta-end 0.018 \
 --predict-type v_prediction \
 --multireso \
 --reso-step 64 \
 --uncond-p 0.22 \
 --uncond-p-t5 0.22\
 --uncond-p-img 0.05\
 --index-file ${index_file} \
 --random-flip \
 --lr ${lr} \
 --batch-size ${batch_size} \
 --image-size ${image_size} \
 --global-seed 999 \
 --grad-accu-steps ${grad_accu_steps} \
 --warmup-num-steps ${warmup_num_steps} \
 --use-flash-attn \
 --use-fp16 \
 --extra-fp16 \
 --results-dir ${results_dir} \
 --resume\
 --resume-module-root ckpts/t2i/model/pytorch_model_module.pt \
 --epochs ${epochs} \
 --ckpt-every ${ckpt_every} \
 --ckpt-latest-every ${ckpt_latest_every} \
 --ckpt-every-n-epoch ${ckpt_every_n_epoch} \
 --log-every 10 \
 --deepspeed \
 --use-zero-stage 2 \
 --gradient-checkpointing \
 --no-strict \
 --training-parts ipadapter \
 --is-ipa True \
 --resume-ipa True \
 --resume-ipa-root ckpts/t2i/model/ipa.pt \
 "$@"

Recommended parameter settings

Inference

Use the following command line for inference.

a. Use the parameter float i-scale to specify the weight of IP-Adapter reference image. The bigger parameter indicates more relativity to reference image.

python3 sample_ipadapter.py --infer-mode fa --ref-image-path ipadapter/input/beach.png --i-scale 1.0 --prompt 一只老虎在海洋中游泳，背景是海洋。构图方式是居中构图，呈现了动漫风格和文化，营造了平静的氛围。 --infer-steps 100 --is-ipa True --load-key module